Why Python:

Simple(Syntax as Simple English), Open Source , Libraries supported by large and Active Community ,

Table of Contents

1. Operators

2. Variables and Variable Naming Conventions

3. Data Types in Python

4. Conditional Statements

5. Looping Statements

6. Functions

7. Packages in Python

8. Hands-on with Pandas Library

1. Operators: Symbolic Representations of Mathematical Tasks

• Arithmetic Operator : +,-,*,-,% ,//, **
• Conditional Operator : Returns True/ False with : <, <=, ==, >=, >, !=
• Logical Operator : and, or, not

Python Commands	Output
3 + 5 / 45-54 * 4	– 212.88888888888889
# "DataScientist"+3 Gives Error "DataScientist "*3	‘DataScientist DataScientist DataScientist ‘
"DataScientist "+ "3 "	‘DataScientist 3 ‘
45>43	True
56<34	False
34*34 > 34*34	False
34*34 == 34**34	False
0 and 3	0
3 and 0	0
3 and 5 # Gives 2nd value (in Python)	5
0 or 3	3
3 or 5 # Returns 5	3
True and False	False
True or False	True

2. Variables and Data Types

• Variables are names bounded to objects
• Case Sensitive, start with character or _underscore (not Number)
• Data Int, Float, Bool, String, (IFBS)

a = 5 a	5
print(a)	5
A=4 # Case Insensitive print(A,a)	4 5
a = 5 b = 7 a = b print(a,b)	7 7
_a5 = 5 type(_a5)	int
b = "Data Scientist" type(b)	str

3. Conditional Statements

• If arrived home early: then cook, else order on swiggy!
• If-else statements: Single Condition

if(condition): statement1 else: statement2 —-	if(time == late): food = swiggy else: food = cook
if(condition1): statement1 elif(condition2): statement2 else: statement3	Assume a variable x, print “positive” if x is greater than 0, ‘Zero’ if x is equal to 0 or “negative” if x is less than 0 x = -23432*-323 if(x == 0): print("X is Zero") elif(x > 0): print("X is Positive") else: print("X is Negative")

# Take a variable X and print "Even" if the number is divisible by 2, otherwise print "Odd" x = 9.3 if(x%2 == 0): print("Given Number x: ", x, "is EVEN ") else: print("Given Number x: ", x, "is Odd ")

# Take a variable y and print "Grade A" if y is greater than 90, "Grade B" # if y is greater than 60 but less than or equal to 90 and "Grade F" otherwise y = 89.1 if(y > 90): print("Grade A, Congratulations") elif(y >60 and y<=90): print("Grade B, All the best") else: print("Grade F, Long way to Go..!")

4. Looping Constructs

For Loop
for i in range(11,50): print(i)	# For Loop to print all the numbers between 10 and 50
for i in range(11,50): if(i%2 != 0): print(i) range(start, stop[, step]) -> range object	# For Loop to print all the ODD numbers between 10 and 50 But Another Option: for i in range(11,50,2):

5. Functions

• Reusable piece of code – created for Solving SPECIFIC Problem

def function_name(inpu_argument): statement1; statement2; return some_var;	def area_circle(radius): area = 3.14*r*r return area
def compare(a,b): if(a>b): greater=a else: greater=b return greater	compare(10,50) >50

6. Python Data Structure

• Existing DataType: int,float, bool, str – can be stored in Single FORMAT only
• 2 Data Structure:

• Lists :– With Sequence : [1,’Python’, 2, ‘is’, 3, ‘Awesome’]
• Dictionaries :- Without Sequence: {‘Ramesh’:150, ‘Sudesh’:160, ‘Suresh’:146}

7. Lists

• Ordered Data Structure – with elements separated by comma – enclosed with Square Brackets
• Extract Single Element: list[index#] # Index
• Extract Sequence: List[0:4] # Starts with 0 and Stops at 3 (not 4-1)
• List Functions: append(), extend([another_list]), remove() , del list[index#],
• Accessing List: for i in list_name: print(list)

# Creating a List marks=[1,2,3,4,5,6,7,8,9,10] marks	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
marks[5] # Index starts at 0	6
# Get Elements till 6 marks[0:6]	[1, 2, 3, 4, 5, 6]
# Adding an element marks.append(11) marks	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
marks.extend([12,13]) marks	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 12, 13]
marks.append([14,15]) marks	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 12, 13, [14, 15]]
# Deleting elements in List marks.remove([14,15]) del marks[0]	# Deleting by Actual # Deleting by Index Value
# Accessing List & Operating on Elements with For for mark in marks: print(mark*100)	200 300 400 …

8. Dictionaries

• Un-Ordered Data Structure – Elements are stored in {Key : Value} pairs
• Add Elements to dictionary with: update() function ; Delete with: del dict(‘Key’)

marks={‘history’:45, ‘Geography’:54, ‘Hindi’:56} marks	{‘Geography’: 54, ‘Hindi’: 56, ‘history’: 45}
marks[‘Geography’]	54
marks[‘english’] = 47 // Adding Elements marks	{‘Geography’: 54, ‘Hindi’: 56, ‘english’: 47, ‘history’: 45}
marks.update({‘Chemistry’:89, ‘Physics’:98}) marks	{‘Chemistry’: 89, ‘Geography’: 54, ‘Hindi’: 56, ‘Physics’: 98, ‘english’: 47, ‘history’: 45}
del marks[‘Hindi’] marks	{‘Chemistry’: 89, ‘Geography’: 54, ‘Physics’: 98, ‘english’: 47, ‘history’: 45}

9. Understanding Standard Libraries in Python

• Built-In Functions provided by ‘Standard Library’
• Module: Single Python File/Class,
• Package: Bundle of Modules
• format: from Package.Module import Function (or)

• from Package import Module => use dot operator for accessing function

• Module.function(x,y)

Data Frames

Reading CSV in Python – Introduction to Pandas

• Pandas: Python Data Analysis Toolkit for READING, FILTERING, MANIPULATING, VISUALIZING and EXPORTING Data
• Different Varieties of Data: CSV, JSON, HTML, Excel …

import pandas as pd
df = pd.read_csv("data.csv")	# Read CSV
df = pd.read_excel("data.csv")	# Read Excel

Data Frames & its Operations

• Data Frame is similar to Excel Tabular datasheet
• (But) Row Index starts from 0
• Some Data Frame(df) functions: df.shape(), df.head()/tail(), df.columns, df[“Column”]

Initial Understanding of Data Frame

df.shape	(891,12) # 891 Rows and 12 Columns
df.head()	df.tail()
df.columns	# Display all Column Name
df.info()
df.describe().transpose()
df[‘Embarked’]	# Get All values of Single Column
df[[‘Embarked’,’Age’]]	# Inside List of Multiple Columns

Indexing a Data Frame

df.iloc[:5]	# Selecting ROWs by their positions Range of Rows : From 0 to 4th Index (5-1) (If comma not available, then all columns)
df.iloc[:,:2] # Select All Rows & Columns from 0 to 1 (2-1)	[Rows, Columns] => [Start_Row : End_Row , Start_Column : End_Col]
df[df[‘Embarked’]==’C’]	# Display All Rows only with EMBARKED=C
df.iloc[:,-2:]	Accessing Last 2 Columns with iloc
df.iloc[-10:,:2]	# Access last 10 rows and first two columns of the index dataframe
df.iloc[24,4] & also df.iloc[24:25,4:5]	# Access Element of 25th Row – 5th Column
df.loc[:,[‘Dependents’,’Education’]]	# Selecting these 2 Columns only